ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective.Life (Basel, Switzerland) · 2024Article
- A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors.Journal of biomedical informatics · 2024Article
- FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modeling.Journal of biomedical informatics · 2023Article
- Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse features by combining the advantages of tree models and neural networks. A two-step debiasing method is tailored for this framework to enhance fairness. Experiments are conducted to analyze unfairness issues in existing models and demonstrate the superiority of our method in both prediction and fairness performance.
Indexed as
Identifiers
37128420PMC10148275What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.